Information Path from Randomness and Uncertainty to Information, Thermodynamics, And Intelligence of Observer

Lerner, Vladimir S.

doi:10.5121/ijit.2014.3404

Cited by 2 publications

(7 citation statements)

References 56 publications

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“…The minimized increments of entropy functional between the cutoffs allow prediction each following cutoff with maximal conditional probability. A sequence of the functional a priori-posteriori probabilities provides Bayesian entropy measuring a probabilistic causally [44][45][46][47], which is transforming to physical casualty in information macrodynamics. Sum of information contributions, extracted from the EF, approaches its theoretical measure (1.1.10) which evaluates the upper limit of the sum.…”

Section: Discussing the Theoretical Resultsmentioning

confidence: 99%

“…The EF imaginary entropy, measured by logarithmic conditional probability of the observing random process, is distinct from Boltzmann physical entropy satisfying Second Thermodynamic Law. The EF-IPF transformations provide the Information Path from Randomness and Uncertainty to Information, Thermodynamics, and Intelligence of Observer [44,45]. This includes a virtual observer, acting with imaginary control and integrating imaginary entropy increments in EF, which is mathematically established in Secs.2.2.5-2.6 and numerically verified in Ses.2.2.8a, b.…”

Section: Time Course On Kmentioning

confidence: 94%

“…This probability measure has applied in [44,47] for the impulse probing of an observable rando m process, which holds opposite Yes-No probabilities -as the unit of probability step-funct ion.…”

Section: The P I Propertiesmentioning

confidence: 99%

“…Step-down ( ) Step functions 2 t u ± (4.2d) starting at 0 k t τ = − contribute the entropy increments on interval ( ) k o τ by moment k t τ = : Relations (6.1-6.6) lead to following specifics of the microprocess. [44,47], which follows the impulse mutual interaction and/or with environment.…”

Section: Sum Of Information Contributionsmentioning

confidence: 99%

“…Optimal control starts with the above initial conditions. The detail identification of information micro-macrodynamics (IMD), based on the [43,44,47], where the dynamics' scale parameter , k m α γ depends on a frequency spectrum of observations detecting through k γ . The observer IMD self-scales the observation to build the observer time-space distributed information network.…”

Section: Since ( )mentioning

confidence: 99%

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The Impulse Interactive Cuts of Entropy Functional Measure on Trajectories of Markov Diffusion Process, Integrating in Information Path Functional, Encoding and Applications

Lerner

2017

BJMCS

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Integrating discrete information, composed of information Bits extracted from observing random process, solves the impulse cutting off entropy functional (EF) measure on trajectories Markov diffusion process whose information integrates path functional (IPF). Each cut brings memory of the entropy being cut, which provides both reduction of the process entropy and discrete unit of the cutting entropy -a Bit. Consequently, information is memorized entropy cutting in random observations which process interactions. The origin of information associates with an "anatomy of creation of impulse" enables both cut and stipulate random process while generating information under the cut. Memory stands as the impulse' cut time interval. Defining the EF via the process additive functional with functions drift and diffusion allows reducing this functional on trajectories to a regular integral functional. Compared to conventional Shannon entropy measure of a random state, cutting the process on separated states decreases quantity of process information by the amount, concealed in correlation connections between these states, which hold hidden process information. The EF-IPF measure integrates information covered in both continuous process and discrete impulses which generate information and transmit it between the impulses.The n -dimensional process cutoff generates a finite information measure, integrated in the IPF whose information approaches the EF measure at n → ∞ , restricting maximal information of the Markov diffusion process. Studied impulse delta-function cutoff and the discrete impulse deliver equivalent information at each cutoff. The constructed finite restriction limits the impulses' discrete stepwise actions applied for cutting the regular integral on the functional increments between the cutoffs. Finite impulse step-up action transfers EF increment to following impulse whose step-down action cuts off information and step-up action starts imaginary (virtual) impulse carrying entropy increment to next real cut.Step-down cut generates maximal information while the step-up action delivers minimal information from impulse cut to next impulse step-down action. A virtual impulse transfers conjugated entropy increments during a microprocess ending with adjoining increment within actual step-down action at cutoff. Extracting maximum of minimal impulse information and transferring minimal entropy between impulses implement maxmin-minimax principle of converting process entropy to information. Each cutoff sequentially and automatically converts entropy to information, holding information Bit from random process, which connects the Bits sequences in the IPF and predicts next cut. The built information macroprocess, as the EF minimax extremal, integrates both imaginary entropy of microprocess and cutoff information of real impulses in IPF information physical process. Each IPF dimensional cut measures Feller kernel information. Cutting entropy memorizes the cutting time interval which freezes the probability of events with re...

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Section: Discussing the Theoretical Resultsmentioning

confidence: 99%

Section: Time Course On Kmentioning

confidence: 94%

Section: The P I Propertiesmentioning

confidence: 99%

Section: Sum Of Information Contributionsmentioning

confidence: 99%

Section: Since ( )mentioning

confidence: 99%

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The Impulse Interactive Cuts of Entropy Functional Measure on Trajectories of Markov Diffusion Process, Integrating in Information Path Functional, Encoding and Applications

Lerner

2017

BJMCS

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Reality, Information, and Information Observer

Lerner

2020

IS4SI 2019 Summit

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Up to now both information and its connection to reality do not have scientifically conclusive definitions, nor implicit origin. They emerge in observing multiple impulses’ interactive yes‒no actions modeling information bits. Each memorized bit of information binds a reversible microprocess with an irreversible information macroprocess along a multi-dimensional observing process. Interactive information dynamics assemble geometrical and information structures of observer cognition and intelligence in double spiral rotating code. Information Path Functional integrates multiple interactive dynamics in finite bits which observe and measure reality. The time and space of reality exists only as discrete units of information. The observed information process, creating its Observer, connects reality, information, and the Observer.

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Information Path from Randomness and Uncertainty to Information, Thermodynamics, And Intelligence of Observer

Cited by 2 publications

References 56 publications

The Impulse Interactive Cuts of Entropy Functional Measure on Trajectories of Markov Diffusion Process, Integrating in Information Path Functional, Encoding and Applications

The Impulse Interactive Cuts of Entropy Functional Measure on Trajectories of Markov Diffusion Process, Integrating in Information Path Functional, Encoding and Applications

Reality, Information, and Information Observer

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